New research from Enterprise Management Associates (EMA) indicates that the first wave of AI adoption in network operations—centered on chatbots and virtual assistants—is giving way to an agentic AI model. In a survey of 458 IT professionals actively using AI in network operations, only 15% expressed a preference for traditional chatbot-style interfaces. These same respondents reported the lowest levels of success with their AI initiatives.
Organizations that are adopting agentic environments—where systems continuously analyze conditions, recommend actions, and collaborate with human operators—are seeing stronger results, according to the EMA data.
The Shift from Chatbots to Agentic AI
“One of the things that we discovered is that the era of chatbots is over. Only 15% said they’re focused on one-on-one interactions with virtual assistants, where you ask a question that gives you information. The people who said that this was their preference are the ones who were getting the least amount of value out of AI,” said Shamus McGillicuddy, research director for network management at EMA, during a recent webinar.
McGillicuddy added, “A little more than a third (33.6%) of them said they wanted AI-enabled collaborative workspaces. There’s an agentic environment that’s sort of telling you what it’s seeing and what it thinks is happening, and what it suggests to do about it, and then the whole team can chat about it too and chat with the agent.”
From Answers to Action
The evolution from chatbots to agentic AI is about taking proactive action. Chatbots operate reactively by replying to questions, whereas agentic AI delivers insights directly embedded into workflows, enabling real-time intervention. According to the EMA research, another 19% of respondents favor proactive systems that flag emerging issues and suggest remediation steps before human operators begin troubleshooting. This capability aligns closely with the ultimate goal of predictive network operations.
“We want to get to a point where AI is telling us there’s something wrong that we should look at. We’d also like to suggest playbooks that it can run to fix those issues. That is very much not a chatbot. That is an agent, an agentic environment,” McGillicuddy said.
Expected Business Benefits
Among the expected business benefits of AI-driven network management are:
- Faster resolution of network problems: 54.1%
- Improve network performance/experience: 51.3%
- Reduced security risk: 48.7%
- Cost optimization: 47.8%
- Proactive problem prevention: 45.9%
- More time available for strategic projects: 41.9%
- Responsiveness to change: 37.8%
- Mitigation of network team’s skills/personnel gaps: 33%
A network infrastructure and operations manager with a Fortune 500 energy company remarked, “I think AI is going to help us respond to incidents quicker. It will help us diagnose yellow flags before they turn into red flags. And it will help us reduce our self-inflicted outages.”
Not Ready for Fully Autonomous Operations
Despite the promise, EMA found that only 35% of enterprises are completely successful with applying AI to network management. Organizations relying on simple interfaces or loosely integrated features are seeing less impact than those embedding AI deeply into workflows and decision-making processes. Furthermore, just 31% of IT professionals say they fully trust the outputs of their AI tools.
The research also highlighted that human oversight remains critical. In related EMA research, 63% of organizations require human approval for AI-driven automation, underscoring the continued reliance on “human-in-the-loop” models.
“No one’s quite ready for autonomous operations, but human in the loop, definitely, maybe human out of the loop in the future for certain things,” McGillicuddy said. He identified several barriers to fully autonomous agentic IT operations: layering humans, systems, and processes together; developing policies and guardrails for compliance and data security; overcoming mistrust and fear; resource gaps in budget and skills; and establishing roles and responsibilities for introducing AI into production environments.
As the industry moves forward, the shift from reactive chatbots to proactive, collaborative agentic AI appears to be the path to greater efficiency and reliability in network management. However, organizations must carefully balance automation with human oversight to build trust and achieve long-term success.
Source: Network World News